Assessment of Deep-Learning Based Motion Compensation on Detection of Perfusion Defects in Cardiac-Gated SPECT Images

Zhang, Xirang; Yang, Yongyi; Pretorius, P. Hendrik; King, Michael A.

doi:10.1109/isbi53787.2023.10230414

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Cited by 2 publications

References 16 publications

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Exploring Anatomical Similarity in Cardiac-Gated Spect Images for A Deep Learning Network

Zhang,

Yang,

Pretorius

et al. 2023

2023 IEEE International Conference on Image Processing (ICIP)

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Exploring Anatomical Similarity in Cardiac-Gated Spect Images for A Deep Learning Network

Zhang,

Yang,

Pretorius

et al. 2023

2023 IEEE International Conference on Image Processing (ICIP)

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DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT

Rahman,

Yu,

Laforest

et al. 2024

IEEE Trans. Radiat. Plasma Med. Sci.

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There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images.To address this need, we build upon concepts from modelobserver theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5% and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic DL-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.

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Assessment of Deep-Learning Based Motion Compensation on Detection of Perfusion Defects in Cardiac-Gated SPECT Images

Cited by 2 publications

References 16 publications

Exploring Anatomical Similarity in Cardiac-Gated Spect Images for A Deep Learning Network

Exploring Anatomical Similarity in Cardiac-Gated Spect Images for A Deep Learning Network

DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT

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